Build a non-end-to-end Text-to-Speech model

[PrasannaKasar]

Problem statement Previous TTS models often produced robotic-sounding speech, mispronounced words, lacked emotional nuance, struggled with contextual understanding, offered limited language support, and provided little customization for voice characteristics.

Solution: A non-end-to-end TTS model with Human-like speech A non-end-to-end TTS model with Transformers using separate components for text processing, phoneme prediction, and waveform generation to enhance pronunciation, prosody, and customization.

Alternatives Alternatives for TTS models include non-end-to-end options like Tacotron 2 and Deep Voice, and end-to-end models such as FastSpeech and WaveRNN. But they are usually computationally expensive and difficult to train.

Additional context Transformer TTS models utilize the self-attention mechanisms of transformers, originally designed for NLP, to improve text-to-speech synthesis by capturing long-range dependencies in text. This architecture enables more natural and expressive speech by decoupling components like text encoding, phoneme prediction, and waveform generation, allowing for greater control over pronunciation and emotional tone. Additionally, attention mechanisms help align text input with audio features, enhancing the accuracy of speech generation

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